Saturday, 27 April 2013

Bootstrapping and evaluating performance

When I first looked at the results of my first classifier I wasn't to exited, since I was detecting not only pedestrians, but also trees, poles, traffic signs, etc. Bootstrapping is a critical operation for enhancing the performance of the detector, and it consists in running the algorithm on negative images, with the purpose of introducing to the classifier the false positive examples as negative ones.

After that I have to find a way for evaluation the performance of my algorithm. In detecting problems this is usually done with ROC curves, which plot the miss rate against the false positive rate.

The INRIA dataset has a training set and a testing set, each with positive-only images and negative-only images. So I am going to use the training set for training my classifier (bootstrapping and all), and then I will run the final classifier in the test set for different thresholds, getting results for miss rate, on the positive-only images, and false positive rate, on the negative-only images.